Arnecke, JörnEck, Sebastian OliverSteuck, PiaVaughan, AlexanderKlein, MaikeKrupka, DanielWinter, CorneliaGergeleit, MartinMartin, Ludger2024-10-212024-10-212024978-3-88579-746-3https://dl.gi.de/handle/20.500.12116/45215This paper focuses on the integration of Generative AI (GenAI) in music theory education, with the specific aim of replicating historical musical styles. Traditional AI applications in music generation assist composers in creating basic compositions but fall short in complex tasks like style imitation. Our project ’Musik-Automat – Mit der KI im musikalischen Frage-Antwort-Spiel’ (’Music Automaton – With AI in a Musical Question-Answer Game’, University of Music Franz Liszt Weimar; project duration: October 2023 - December 2024 [Un23]) addresses this gap by developing a GenAI capable of generating and understanding symbolic musical data, allowing for a dialogic interaction between students and AI. This interaction aims to enhance educational and creative processes by enabling students to engage in a musical question-and-answer game, promoting both stylistic knowledge and creativity. We create an educational web application that supports composing in historical styles. Our methodology emphasizes human-machine collaboration, where human feedback guides the music generation model, ensuring the human role remains central to artistic innovation and the creative process. The project is designed to be inclusive of various skill levels and classical musical genres, with a focus on practical application in music theory education. Initial experiments demonstrate that this interactive model stimulates critical thinking and classroom discussions about musical style and authenticity, therefore augmenting the overall learning experience. Supported by the Stifterverband and the Thuringian Ministry of Economics, Science and Digital Society [St23], this project aims to support digital transformation in higher education, preparing students to effectively use AI in academic and later professional environments.enGenerative AIMusic CompositionMusic EducationAI in Creative ProcessesHuman-AI InteractionMachine Learning in MusicSymbolic Music DataMusic Theory InnovationDigital Transformation in EducationMusic TechnologyText/Conference Paper10.18420/inf2024_541617-5468